{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/defocus-blur-detection-via-multi-stream","title":"Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Wenda Zhao","Fan Zhao","Dong Wang","Huchuan Lu"],"abstract":"Defocus blur detection (DBD) is the separation of infocus and out-of-focus regions in an image. This process has been paid considerable attention because of its remarkable potential applications. Accurate differentiation of homogeneous regions and detection of low-contrast focal regions, as well as suppression of background clutter, are challenges associated with DBD. To address these issues, we propose a multi-stream bottom-top-bottom fully convolutional network (BTBNet), which is the first attempt to develop an end-to-end deep network for DBD. First, we develop a fully convolutional BTBNet to integrate low-level cues and high-level semantic information. Then, considering that the degree of defocus blur is sensitive to scales, we propose multi-stream BTBNets that handle input images with different scales to improve the performance of DBD. Finally, we design a fusion and recursive reconstruction network to recursively refine the preceding blur detection maps. To promote further study and evaluation of the DBD models, we construct a new database of 500 challenging images and their pixel-wise defocus blur annotations. Experimental results on the existing and our new datasets demonstrate that the proposed method achieves significantly better performance than other state-of-the-art algorithms.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Zhao_Defocus_Blur_Detection_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhao_Defocus_Blur_Detection_CVPR_2018_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"defocus-blur-detection","task_name":"Defocus Blur Detection"},{"task_slug":"defocus-estimation","task_name":"Defocus Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/defocus-estimation-on-cuhk-blur-detection","task":"Defocus Estimation","dataset":"CUHK - Blur Detection Dataset","model":"BTBNet (3S + FNet + RRNet)","rank_in_archive_order":4,"of":5,"metrics":{"F-measure":"0.867","MAE":"0.107"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}